Systems and methods for utilizing machine learning and feature selection to classify driving behavior

ABSTRACT

A device may receive vehicle operation data associated with operation of a plurality of vehicles, and may process the vehicle operation data to generate processed vehicle operation data. The device may extract multiple features from the processed vehicle operation data, and may train machine learning models, with the multiple features, to generate trained machine learning models that provide model outputs. The device may process the multiple features, with a feature selection model and based on the model outputs, to select sets of features from the plurality of features, and may process the sets of features, with the trained machine learning models, to generate indications of driving behavior and reliabilities of the indications. The device may select a set of features, from the sets of features, based on the indications and the reliabilities, where the set of features may be calculated by a device associated with a particular vehicle.

RELATED APPLICATION

This application claims priority to European Patent Application No.19205766.9, filed on Oct. 28, 2019, entitled “SYSTEMS AND METHODS FORUTILIZING MACHINE LEARNING AND FEATURE SELECTION TO CLASSIFY DRIVINGBEHAVIOR,” which is hereby expressly incorporated by reference herein.

BACKGROUND

Driver behavior classification is a problem of great interest in theintelligent transportation community and is a key component of severalapplications. First of all, driving style, in combination with otherfactors such as road type and traffic congestion, has a substantialimpact on fuel consumption. Thus, detecting high consumption maneuversand coaching drivers to avoid them can lead to significant reductions incost and carbon dioxide emissions. Furthermore, driver behaviorclassification is closely related to road safety since aggressivedrivers tend to operate vehicles in an unsafe manner (e.g., withexcessive speed, short car-following distance, erratic lane changes, andimprudent maneuvers), thus increasing risks of road accidents.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1K are diagrams of one or more example implementationsdescribed herein.

FIG. 2 is a diagram of an example environment in which systems and/ormethods described herein may be implemented.

FIG. 3 is a diagram of example components of one or more devices of FIG.2.

FIG. 4 is a flow chart of an example process for utilizing machinelearning and feature selection to classify driving behavior.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

Vehicle operation data (e.g., driving data) is generally collected usingone or multiple tracking devices installed inside a vehicle. Suchtracking devices generally include an inertial measurement unit (IMU), athree-axis accelerometer, a global positioning system (GPS) device,and/or the like. More advanced tracking devices have access to vehiclesensors, via an engine control module (ECM), that collect informationassociated with pedal positions, steering angle, enginerevolutions-per-minute, and/or the like.

Driving behavior may be classified utilizing all vehicle operation datafrom all vehicle tracking devices and collecting the vehicle operationdata from the vehicle tracking devices at a maximum sampling frequency.However, transmission and storage of such vehicle operation data in somedomains (e.g., a fleet management company monitoring several millions ofvehicles) can be daunting and expensive. Furthermore, vehicle trackingdevices have limited computational power and are unable to performadvanced computations required for classification of driving behavior.Thus, current techniques for classifying driving behavior wastecomputing resources (e.g., processing resources, memory resources,and/or the like), communication resources, networking resources, and/orthe like associated with capturing vehicle operation data, transmittingthe vehicle operation data, storing the vehicle operation data, and/orthe like.

Some implementations described herein provide a vehicle platform thatutilizes machine learning and feature selection to classify drivingbehavior. For example, the vehicle platform may receive vehicleoperation data associated with operation of multiple vehicles, and mayprocess the vehicle operation data to generate processed vehicleoperation data for a particular time period. The vehicle platform mayextract multiple features from the processed vehicle operation data, andmay train machine learning models, with the multiple features, togenerate trained machine learning models. The vehicle platform maygenerate model outputs based on training the machine learning models,and may process the multiple features, with a feature selection modeland based on the model outputs, to select sets of features from themultiple features. The vehicle platform may process the sets offeatures, with the trained machine learning models, to generateindications of driving behavior and reliabilities of the indications ofdriving behavior, and may select a set of features, from the sets offeatures, based on the indications of driving behavior and thereliabilities of the indications of driving behavior. A user deviceassociated with a particular vehicle may be capable of calculating theset of features for the particular time period.

In this way, the vehicle platform identifies particular features ofvehicle operation data to be captured by vehicle devices for aparticular time period, and utilizes the particular features to classifydriving behavior. A quantity of the particular features and theparticular time period ensure that the vehicle devices can process thevehicle operation data and transmit the particular features to thevehicle platform. Thus, the vehicle platform conserves computingresources (e.g., processing resources, memory resources, and/or thelike), communication resources, networking resources, and/or the likethat would otherwise be wasted in capturing vehicle data, transmittingthe vehicle data, storing the vehicle data, and/or the like.

FIGS. 1A-1K are diagrams of one or more example implementations 100described herein. As shown in FIG. 1A, user devices 105 may beassociated with vehicles 110 and a vehicle platform 115. In someimplementations, user devices 105 may include sensors that capturevehicle operation data (e.g., data indicating acceleration, speed,movement, and/or the like) associated with vehicles 110 and/or mayreceive the vehicle operation data from vehicle sensors of vehicles 110,an engine control module (ECM) of vehicles 110, and/or the like.

As further shown in FIG. 1A, and by reference number 120, vehicleplatform 115 may receive, from user devices 105 associated with vehicles110, vehicle operation data associated with operation of the vehicles.In some implementations, the vehicle operation data may include dataidentifying accelerations of vehicles 110, speeds of vehicles 110,distances of vehicles 110 from other vehicles, pedal positionsassociated with vehicles 110 (e.g., brake pedals and acceleratorpedals), steering angles associated with vehicles 110, enginerevolutions-per-minute (RPMs) of vehicles 110, and/or the like.

In some implementations, vehicle platform 115 may receive the vehicleoperation data from user devices 105, from inertial measurement unitsassociated with vehicles 110, from three-axis accelerometers associatedwith vehicles 110, from global positioning system (GPS) devicesassociated with vehicles 110, from ECMs associated with vehicles 110,from video cameras associated with vehicles 110, and/or the like.Vehicle platform 115 may periodically receive the vehicle operationdata, may continuously receive the vehicle operation data, may receivethe vehicle operation data based on a request, and/or the like. Vehicleplatform 115 may store the vehicle operation data in a data structure(e.g., a database, a table, a list, and/or the like) associated withvehicle platform 115.

As shown in FIG. 1B, and by reference number 125, vehicle platform 115may process the vehicle operation data to generate processed vehicleoperation data for a particular time period. The particular time periodmay be a time period measured in seconds (e.g., ten seconds, thirtyseconds, sixty seconds, and/or the like), in minutes (e.g., two minutes,five minutes, and/or the like), and/or the like. In someimplementations, when processing the vehicle operation data, vehicleplatform 115 may apply a moving average to the vehicle operation data togenerate a low-pass version of the vehicle operation data, and may addthe low-pass version of the vehicle operation data to the vehicleoperation data to generate the processed vehicle operation data. Forexample, the vehicle operation data may include raw accelerometer datathat includes a non-negligible amount of noise (e.g., mid-to-highfrequency noise due to engine vibration, rugged road vibration, and/orthe like). Vehicle platform 115 may process the raw accelerometer data,by applying a low-pass filter that employs the moving average, togenerate a low-pass version of the accelerometer data. Vehicle platform115 may add the low-pass version of the accelerometer data and the rawaccelerometer data to generate the processed vehicle operation data.

In this way, vehicle platform 115 may generate processed vehicleoperation data that includes values for each of a quantity of inputvariables that represent filtered data as well as raw data. For example,the input variables may include an x-axis raw acceleration variable, ay-axis raw acceleration variable, a z-axis raw acceleration variable, anx-axis filtered acceleration variable, a y-axis filtered accelerationvariable, a z-axis filtered acceleration variable, a GPS speed variable,and/or the like.

As shown in FIG. 1C, and by reference number 130, vehicle platform 115may extract a plurality of features from the processed vehicle operationdata. In some implementations, when extracting the plurality offeatures, vehicle platform 115 may compute, based on the processedvehicle operation data, a plurality of statistics that correspond to theplurality of features. For example, vehicle platform 115 may compute avalue of a statistical variable for each input variable included in theprocessed vehicle operation data, and may extract a feature for eachcombination of a particular input variable and a different statisticalvariable associated with the particular input variable.

The statistical variables may include a mean variable, a variancevariable, a standard deviation variable, a maximum variable, a minimumvariable, a range variable, an interquartile range variable, a skewnessvariable, a kurtosis variable, a slope variable, a median variable, atwenty-fifth percentile variable, a seventy-fifth percentile variable,and/or the like. In one example, vehicle platform 115 may extract afeature associated with a mean of the x-axis raw acceleration, a featureassociated with a variance of the x-axis raw acceleration, a featureassociated with a standard deviation of the x-axis raw acceleration,and/or the like for all statistical variables associated with the x-axisraw acceleration. Vehicle platform 115 may extract a feature associatedwith a mean of the y-axis raw acceleration, a feature associated with avariance of the y-axis raw acceleration, a feature associated with astandard deviation of the y-axis raw acceleration, and/or the like forall statistical variables associated with the y-axis raw acceleration.In this way, vehicle platform 115 may extract a set of features based oncommonly available and readily obtainable and/or determinableinformation and based on statistics that can be efficiently calculatedwithout requiring a large amount of computing resources.

As shown in FIG. 1D, and by reference number 135, vehicle platform 115may train machine learning models, with the plurality of features, togenerate trained machine learning models. The trained machine learningmodels may generate indications of driving behaviors (e.g., normaldriving behavior, reckless driving behavior, and/or the like) based onthe features.

In some implementations, when training the machine learning models withthe plurality of features, vehicle platform 115 may utilize a nestedcross-validation to tune a plurality of parameters for the machinelearning models and to evaluate the machine learning models. Vehicleplatform 115 may perform a preliminary analysis, with a first randomforest model (e.g., a baseline random forest model), to tune theplurality of parameters, and may utilize a second random forest model(e.g., an evaluation random forest model) to classify the plurality ofparameters and to generate the trained machine learning models. Asfurther shown in FIG. 1D, and by reference number 140, vehicle platform115 may generate model outputs based on training the machine learningmodels. The model outputs may include indications of reliabilitiesassociated with the indications of driving behaviors.

When utilizing the nested cross-validation to tune the plurality ofparameters for the machine learning models and to evaluate the machinelearning models, vehicle platform 115 may employ a nested leave-one-outcross-validation for a driver, of a plurality of drivers, associatedwith the vehicle operation data. For example, the nested leave-one-outcross-validation may include an outer loop that generates multipletraining and/or test splits by iteratively selecting data from onedriver as a test set and selecting remaining data as a training set.Where d is the number of drivers, for each of the d splits, vehicleplatform 115 may determine a reliability of data relative to the testdriver, and may average the reliabilities to generate a testreliability. The nested leave-one-out cross-validation may include aninner loop that iterates over the drivers selected in the training setand generates training and/or validation splits. For each of the d-1splits, vehicle platform 115 may determine reliabilities associated withthe validation set, and may average scores of different splits of theinner loop to generate a validation accuracy.

In some implementations, the machine learning models may include randomforest machine learning models. The random forest machine learningmodels may be utilized by vehicle platform 115 to determine whether adriver is exhibiting normal driving behavior or aggressive drivingbehavior based on vehicle operation data.

In some implementations, vehicle platform 115 may train the machinelearning models, with historical feature information (e.g., informationidentifying the plurality of features), to determine indications ofdriving behavior. For example, vehicle platform 115 may separate thehistorical feature information into a training set, a validation set, atest set, and/or the like. The training set may be utilized to train themachine learning models. The validation set may be utilized to validateresults of the trained machine learning models. The test set may beutilized to test operation of the machine learning models.

In some implementations, vehicle platform 115 may train the machinelearning models using, for example, an unsupervised training procedureand based on the historical feature information. For example, vehicleplatform 115 may perform dimensionality reduction to reduce thehistorical feature information to a minimum feature set, therebyreducing resources (e.g., processing resources, memory resources, and/orthe like) to train the machine learning models, and may apply aclassification technique to the minimum feature set.

In some implementations, vehicle platform 115 may use a logisticregression classification technique to determine a categorical outcome(e.g., that particular historical feature information indicatesparticular driving behaviors). Additionally, or alternatively, vehicleplatform 115 may use a naive Bayes classifier technique. In this case,vehicle platform 115 may perform binary recursive partitioning to splitthe historical feature information into partitions and/or branches anduse the partitions and/or branches to determine outcomes (e.g., thatparticular historical feature information indicates particular drivingbehaviors). Based on using recursive partitioning, vehicle platform 115may reduce utilization of computing resources relative to manual, linearsorting and analysis of data points, thereby enabling use of thousands,millions, or billions of data points to train the machine learningmodel, which may result in a more accurate model than using fewer datapoints.

Additionally, or alternatively, vehicle platform 115 may use a supportvector machine (SVM) classifier technique to generate a non-linearboundary between data points in the training set. In this case, thenon-linear boundary is used to classify test data into a particularclass.

Additionally, or alternatively, vehicle platform 115 may train themachine learning models using a supervised training procedure thatincludes receiving input to the machine learning models from a subjectmatter expert, which may reduce an amount of time, an amount ofprocessing resources, and/or the like to train the machine learningmodels relative to an unsupervised training procedure. In someimplementations, vehicle platform 115 may use one or more other modeltraining techniques, such as a neural network technique, a latentsemantic indexing technique, and/or the like. For example, vehicleplatform 115 may perform an artificial neural network processingtechnique (e.g., using a two-layer feedforward neural networkarchitecture, a three-layer feedforward neural network architecture,and/or the like) to perform pattern recognition with regard to patternsof the historical feature information. In this case, using theartificial neural network processing technique may improve an accuracyof the trained machine learning models generated by vehicle platform 115by being more robust to noisy, imprecise, or incomplete data, and byenabling vehicle platform 115 to detect patterns and/or trendsundetectable to human analysts or systems using less complex techniques.

In some implementations, rather than training the machine learningmodels, vehicle platform 115 may receive a trained machine learningmodels from another device (e.g., a server device). For example, aserver device may generate the trained machine learning models based onhaving trained machine learning models in a manner similar to thatdescribed above, and may provide the trained machine learning models tovehicle platform 115 (e.g., may pre-load vehicle platform 115 with thetrained machine learning models, may receive a request from vehicleplatform 115 for the trained machine learning models, and/or the like).

As shown in FIG. 1E, and by reference number 145, vehicle platform 115may process the plurality of features, with a feature selection modeland based on the model outputs, to select sets of features from theplurality of features. In some implementations, when processing theplurality of features, vehicle platform 115 may estimate an importance(e.g., a Gini importance) of each of the plurality of features, and mayselect the sets of features from the plurality of features based on theimportance estimated for each of the plurality of features. For example,starting from a trained random forest model with a quantity (F) oftrees, vehicle platform 115 may calculate a Gini importance (Imp) for afeature (X) as:

${{Imp}\left( X_{i} \right)} = {\frac{1}{F}{\sum\limits_{t = 1}^{F}\;{\Sigma_{n \in \varphi_{t}}\mspace{14mu} 1\mspace{14mu}{\left\{ {i \in s_{n}} \right\}\mspace{14mu}\left\lbrack {\frac{N_{n}}{N}\mspace{14mu}\Delta\; i\mspace{14mu}\left( {s_{n},n} \right)} \right\rbrack}}}}$${1\mspace{14mu}\left\{ {i \in s_{n}} \right\}} = \left\{ \begin{matrix}1 & {{if}\mspace{14mu} X_{i}\mspace{14mu}{selected}\mspace{14mu}{by}\mspace{14mu}{split}\mspace{14mu} s_{n}} \\0 & {{otherwise}\mspace{169mu}}\end{matrix} \right.$

where φ_(t) may correspond to a set of nodes in a tree t, N_(n) maycorrespond to a number of training samples that fall in a subtree of anode n, and N may correspond to a total number of samples. Given a Giniindex (i_(G)):

${i_{G}(n)} = {\sum\limits_{k = 1}^{C}\;{\frac{N_{n_{k}}}{N_{n}}\left( {1 - \frac{N_{n_{k}}}{N_{n}}} \right)}}$

an impurity decrease generated by split s_(n) on a node n may be definedas:

${\Delta\;{i\left( {s_{n},n} \right)}} = {{i_{G}(n)} - {\frac{N_{n_{L}}}{n_{n}}\mspace{14mu} i_{G}\mspace{14mu}\left( n_{L} \right)} - {\frac{N_{n_{R}}}{N_{n}}\mspace{14mu} i_{G}\mspace{14mu}\left( n_{R} \right)}}$

where C may correspond to a number of classes, N_(n) _(k) may correspondto a number of samples of a class k falling in a node n' s subtree,n_(L) and n_(R) may correspond to, respectively, left and right childrenof the node n, and N_(n) _(L) and N_(n) _(R) may correspond to a numberof training samples falling in left and right subtrees of the node n.

In some implementations, when processing the plurality of features,vehicle platform 115 may rank each of the plurality of features, basedon an average Gini importance associated with each of the plurality offeatures, to generate rankings of the plurality of features. Vehicleplatform 115 may filter the rankings of the plurality of features togenerate filtered rankings of the plurality of features, and maydetermine validation accuracies associated with the plurality offeatures based on the filtered rankings of the plurality of features.Vehicle platform 115 may select the sets of features, from the pluralityof features, based on the validation accuracies associated withplurality of features.

When filtering the rankings of the plurality of features, vehicleplatform 115 may execute a filtering model to remove highly correlatedvariables as follows:

  function FILTERRANKING (r, c)  i ← 0  while i <= len(r) do 

  iterate on the ranking   top ← r[i]   j ← i+1   while j <= len(r) do   other ← r[j]    if Corr(top, other) > c then     r ← r \ other   else     j ← j+1  return r.As shown above, the filtering model may begin with a feature rankedhighest based on the Gini importance, as described above, and may removeall features that have a correlation (e.g., an absolute Pearsoncorrelation) that exceeds a given threshold (c). The filtering model mayselect a next highest ranked feature from the remaining features, andmay iterate the method until all features have been selected or filteredout. Vehicle platform 115 may re-rank the remaining features by traininga first random forest model on the remaining features. In someimplementations, vehicle platform 115 may assess several differentvalues for the threshold c, each of which may generate a differentfiltered ranking.

As shown in FIG. 1F, and by reference number 150, vehicle platform 115may process the sets of features, with the trained machine learningmodels, to generate indications of driving behavior and reliabilities ofthe indications. In some implementations, each of the indications ofdriving behavior may include information identifying aggressive drivingbehavior, information identifying normal driving behavior, informationidentifying drowsy driving behavior, information identifying performancedriving behavior, information identifying economical driving behavior,and/or the like. In some implementations, vehicle platform 115 may traina second random forest model based on different subsets of features. Forexample, vehicle platform 115 may begin with a feature ranked highestbased on the Gini importance, as described above, and may incrementallyadd features in decreasing order of importance. At each increment,vehicle platform 115 may train the second random forest model, mayutilize tuning parameters with the second random forest model, and mayevaluate a reliability of the second random forest model based onindependent data via cross-validation, as described above.

As shown in FIG. 1G, and by reference number 155, vehicle platform 115may select a set of features, from the sets of features, based on theindications of driving behavior and the reliabilities of theindications. In some implementations, vehicle platform 115 may utilize afeature selection model to select the set of features from the sets offeatures. For example, given the plurality of features and a set offiltering thresholds (cSet), the feature selection model may perform thefollowing steps:

  procedure FEATURESELECTION (allFeatures, cSet)  ref Ranking, _ ← train(baseRF, allFeatures)  reliabilities ← Ø  for c in cSet do   fr ←FILTERRANKING (refRanking, c)   filteredRanking, _ ← train (baseRF, fr)  features ← Ø   reliabilites_(c) ← Ø   for f in filteredRanking do   features ← features ∪ f    _, a ← train(evalRF, features)   reliabilities_(c) ← reliabilities_(c) ∪ a   reliabilities ←reliabilities ∪ reliabilities_(c)  return (features, reliabilities)where baseRF may correspond to a first (e.g., a baseline) random forestmodel and evalRF may correspond to a second (e.g., an evaluation) randomforest model. The train(⋅) function may train a specified random forestmodel based on a particular set of features (e.g. allFeatures) and mayreturn a ranking of input features and a validation accuracy.

In one example, vehicle platform 115 may utilize features based oncombinations of the input variables and the statistical variables, asdescribed above, and may select a particular quantity (e.g., three, six,and/or the like) of features, such as a standard deviation of a z-axis,a mean of a vehicle speed, a skewness of a y-axis filtered acceleration,and/or the like. The selected features may be representative ofdiscriminative driving characteristics. For example, the standarddeviation of the z-axis (e.g., a longitudinal acceleration) may indicateextreme braking and accelerations, the mean of the vehicle speed (e.g.,an average speed) may be associated with a tendency of speeding, and theskewness of y-axis filtered acceleration (e.g., a lateral acceleration)may indicate harsh cornering in curves and turns. In this way, vehicleplatform 115 may select a quantity of features that may be determined bya device with limited computing power, such as by user device 105 and/ora computing device associated with vehicle 110.

As shown in FIG. 1H, and by reference number 160, vehicle platform 115may provide, to user device 105 associated with vehicle 110, a requestto calculate the selected set of features for a particular time period.The particular time period may be time period measured in seconds (e.g.,ten seconds, thirty seconds, sixty seconds, and/or the like), in minutes(e.g., two minutes, five minutes, and/or the like), and/or the like. Asfurther shown in FIG. 1H, and by reference number 165, user device 105may receive vehicle operation data, from vehicle 110 and/or sensorsassociated with vehicle 110, for the particular time period and based onthe request. For example, user device 105 may calculate the vehicleoperation data (e.g., based on sensors associated with user device 105)and/or may receive the vehicle operation data from a inertialmeasurement unit associated with vehicle 110, from a three-axisaccelerometer associated with vehicle 110, from a GPS device associatedwith vehicle 110, from an ECM associated with vehicle 110, from videocameras associated with vehicle 110, and/or the like.

In some implementations, the selected set of features may be differentfor different vehicles 110 and/or based on conditions associated withthe different vehicles 110. For example, a vehicle 110 may not include adevice that is capable of capturing the vehicle operation data requiredto determine the set of features. In such an example, vehicle platform115 may receive an indication of vehicle operation data that isavailable to vehicle 110 and may determine a particular set of featuresbased on the available vehicle operation data. Other factors mayinfluence selection of the set of features, such as a type of vehicle110 calculating the set of features (e.g., a truck, a car, a motorcycle,and/or the like); a location of vehicle 110 (e.g., a highway, a cityroad, a country road, and/or the like); environmental conditionsassociated with vehicle (e.g., sunny, rainy, snowy, icy, and/or thelike); and/or the like. In some implementations, vehicle platform 115may receive information identifying one or more of the other factors andmay determine a particular set of features based on the one or more ofthe other factors. With reference to FIG. 1H, vehicle platform 115 maythen provide, to user device 105, a request to calculate the particularset of features for the particular time period.

As further shown in FIG. 1H, and by reference number 170, user device105 may calculate the selected set of features for the particular timeperiod based on the vehicle operation data. Alternatively, oradditionally, user device 105 may automatically calculate the selectedset of features on a periodic basis (e.g., every quantity of seconds)and may continuously and periodically provide the calculated set offeatures to vehicle platform 115 in near real-time relative tocalculating the selected set of features.

As shown in FIG. 11, and by reference number 175, vehicle platform 115may receive, from user device 105, data identifying the selected set offeatures for the particular time period. In some implementations,vehicle platform 115 may receive a single data point for each feature ofthe selected set of features over the particular time period.Alternatively, or additionally, vehicle platform 115 may continuouslyand periodically receive data points for each feature of the selectedset of features when such features are provided on a periodic basis.

As shown in FIG. 1J, and by reference number 180, vehicle platform 115may process the selected set of features, with one or more of thetrained machine learning models, to generate an indication of drivingbehavior associated with vehicle 110 (e.g., a drive of vehicle 110). Inthis way, vehicle platform 115 may distinguish between safe or normaldriving behavior and aggressive driving behavior while using only alimited set of selected features. The limited set of features may becalculated with limited computing resources (e.g., by user device 105and/or by devices of vehicle 110) to provide an effective and accurateindication of driving behavior. This may reduce computations andcommunications required to determine driving behavior associated withvehicle 110, which may conserve computing resources, communicationresources, networking resources, and/or the like that would otherwise bewasted in capturing all available vehicle operation data, transmittingthe vehicle operation data, storing the vehicle operation data, and/orthe like.

As shown in FIG. 1K, and by reference number 185, vehicle platform 115may perform one or more actions based on the indication of drivingbehavior. In some implementations, the one or more actions may includevehicle platform 115 providing, to user device 105, the indication ofdriving behavior associated with vehicle 110. For example, vehicleplatform 115 may provide the indication for display on user device 105or on another device associated with vehicle 110; may provide theindication for display to a driver of vehicle 110, to an employer of thedriver, to an owner of vehicle 110, and/or the like. In this way,vehicle platform 115 may enable the user of user device 105, the driverof vehicle 110, an employer of the driver, a parent of the driver,and/or the like, to be aware of adverse driving behavior. This mayenable the driver to effectively adjust and/or improve drivingtechniques, which may improve road safety, conserve fuel, conserveresources that would otherwise be wasted policing poor driving behavior,handling vehicle accidents, and/or the like.

In some implementations, the one or more actions may include vehicleplatform 115 providing the indication of driving behavior to a deviceassociated with an insurer of vehicle 110. In this way, vehicle platform115 may enable the insurer to adjust coverage and/or cost of coverageassociated with vehicle 110, an owner of vehicle 110, a driver ofvehicle 110, and/or the like. The insurer may provide guidance and/ortraining to the driver of vehicle 110 (e.g., in exchange for notincreasing coverage costs) in order to reduce risks of accidents,tickets, and/or the like, which may improve road safety, conserve fuel,conserve resources that would otherwise be wasted handling insuranceclaims, policing poor driving behavior, handling vehicle accidents,and/or the like.

In some implementations, the one or more actions may include vehicleplatform 115 providing, to user device 105, an instruction to addressthe driving behavior. For example, vehicle platform 115 may instruct thedriver of vehicle 110 (e.g., in near real-time) to adjust a drivingspeed, to stop making reckless turns, and/or the like. In this way,vehicle platform 115 may enable the driver of vehicle 110 to performcorrective driving actions based on the indication of driving behaviorassociated with vehicle 110, which may improve road safety, conservefuel, conserve resources that would otherwise be wasted policing poordriving behavior, handling vehicle accidents, and/or the like.

In some implementations, the one or more actions may include vehicleplatform 115 causing vehicle 110 to be disabled or the speed of vehicle110 to be reduced to a speed limit based on the indication of drivingbehavior (e.g., that the driver was speeding). In this way, vehicleplatform 115 may prevent vehicle 110 from being operated in a dangerousmanner that risks death or injury (e.g., to the driver of vehicle 110,passengers of vehicle 110, other drivers, and/or the like), that risksdamage to property (e.g., damage to vehicle 110, damage to othervehicles, damage to physical property, and/or the like), and/or thelike. This may conserve resources that would otherwise be wasted intreating injuries, repairing damage, handling vehicle accidents,handling legal actions, and/or the like.

In some implementations, the one or more actions may include vehicleplatform 115 determining a driver risk score based on the indication ofdriving behavior. In this way, vehicle platform 115 may enable a driverof vehicle 110, an employer of the driver, an owner of vehicle 110,and/or the like to monitor quality and assess risk associated with thedriver and/or operation of vehicle 110; to be aware of changes todriving behavior associated with vehicle 110; to provide an objectiveand consistent mechanism for evaluating driver performance, rewardinggood driver performance, addressing bad driver performance, etc.; and/orthe like.

In some implementations, the one or more actions may include vehicleplatform 115 retraining the machine learning models based on theindication of driving behavior. In this way, vehicle platform 115 mayimprove the accuracy of the machine learning models in determiningindications of driving behaviors, reliabilities of the indications,and/or the like, which may improve speed and efficiency of the machinelearning models and conserve computing resources, network resources,and/or the like.

In some implementations, the one or more actions may include vehicleplatform 115 causing a driver training program, focused on the drivingbehavior, to be scheduled for the driver of vehicle 110. In this way,vehicle platform 115 may automatically arrange and/or facilitatetraining that may improve driving behavior, without requiring manualadministrative actions or other resources to arrange for and/orfacilitate the driver training program. This may conserve resources thatwould otherwise be wasted treating injuries, repairing damage, handlingvehicle accidents, handling legal actions, and/or the like caused by thedriving behavior.

In some implementations, the one or more actions may include vehicleplatform 115 causing vehicle 110 to operate in an autonomous mode untilthe driver of the particular vehicle completes particular training. Forexample, vehicle 110 may include an autonomous mode, and vehicleplatform 115 may cause the autonomous mode to be engaged, and preventthe drive from operating vehicle 110 until receiving an indication thatthe driver has completed the particular training. In this way, vehicleplatform 115 may conserve resources that would otherwise be wastedtreating injuries, repairing damage, handling vehicle accidents,handling legal actions, and/or the like caused by the driver.

In this way, several different stages of the process for classifyingdriving behavior is automated via machine learning and featureselection, which may remove human subjectivity and waste from theprocess, and which may improve speed and efficiency of the process andconserve computing resources (e.g., processing resources, memoryresources, and/or the like), communication resources, networkingresources, and/or the like. Furthermore, implementations describedherein use a rigorous, computerized process to perform tasks or rolesthat were not previously performed or were previously performed usingsubjective human intuition or input. For example, currently there doesnot exist a technique that utilizes machine learning and featureselection to classify driving behavior. Finally, the process forclassifying driving behavior conserves computing resources,communication resources, networking resources, and/or the like thatwould otherwise be wasted in capturing all available vehicle operationdata, transmitting the vehicle operation data, storing the vehicleoperation data, and/or the like.

As indicated above, FIGS. 1A-1K are provided merely as examples. Otherexamples may differ from what was described with regard to FIGS. 1A-1K.The number and arrangement of devices and networks shown in FIGS. 1A-1Kare provided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIGS. 1A-1K. Furthermore, two or more devices shown in FIGS.1A-1K may be implemented within a single device, or a single deviceshown in FIGS. 1A-1K may be implemented as multiple, distributeddevices. Additionally, or alternatively, a set of devices (e.g., one ormore devices) of FIGS. 1A-1K may perform one or more functions describedas being performed by another set of devices of FIGS. 1A-1K.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods described herein may be implemented. As shown in FIG. 2,environment 200 may include user device 105, a vehicle platform 115, anda network 230. Devices of environment 200 may interconnect via wiredconnections, wireless connections, or a combination of wired andwireless connections.

User device 105 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information, such asinformation described herein. For example, user device 105 may include amobile phone (e.g., a smart phone, a radiotelephone, etc.), a laptopcomputer, a tablet computer, a desktop computer, a handheld computer, agaming device, a wearable communication device (e.g., a smart watch, apair of smart glasses, a heart rate monitor, a fitness tracker, smartclothing, smart jewelry, a head mounted display, etc.), a GPS device, adevice included in vehicle 110 (e.g., an inertial measurement unit, athree-axis accelerometer, a GPS device, an ECM, a video camera, and/orthe like) or a similar type of device. In some implementations, userdevice 105 may receive information from and/or transmit information tovehicle platform 115.

Vehicle platform 115 includes one or more devices that utilize machinelearning and feature selection to classify driving behavior. In someimplementations, vehicle platform 115 may be designed to be modular suchthat certain software components may be swapped in or out depending on aparticular need. As such, vehicle platform 115 may be easily and/orquickly reconfigured for different uses. In some implementations,vehicle platform 115 may receive information from and/or transmitinformation to one or more user devices 105.

In some implementations, as shown, vehicle platform 115 may be hosted ina cloud computing environment 210. Notably, while implementationsdescribed herein describe vehicle platform 115 as being hosted in cloudcomputing environment 210, in some implementations, vehicle platform 115may not be cloud-based (i.e., may be implemented outside of a cloudcomputing environment) or may be partially cloud-based.

Cloud computing environment 210 includes an environment that hostsvehicle platform 115. Cloud computing environment 210 may providecomputation, software, data access, storage, etc., services that do notrequire end-user knowledge of a physical location and configuration ofsystem(s) and/or device(s) that hosts vehicle platform 115. As shown,cloud computing environment 210 may include a group of computingresources 220 (referred to collectively as “computing resources 220” andindividually as “computing resource 220”).

Computing resource 220 includes one or more personal computers,workstation computers, mainframe devices, or other types of computationand/or communication devices. In some implementations, computingresource 220 may host vehicle platform 115. The cloud resources mayinclude compute instances executing in computing resource 220, storagedevices provided in computing resource 220, data transfer devicesprovided by computing resource 220, etc. In some implementations,computing resource 220 may communicate with other computing resources220 via wired connections, wireless connections, or a combination ofwired and wireless connections.

As further shown in FIG. 2, computing resource 220 includes a group ofcloud resources, such as one or more applications (“APPs”) 220-1, one ormore virtual machines (“VMs”) 220-2, virtualized storage (“VSs”) 220-3,one or more hypervisors (“HYPs”) 220-4, and/or the like.

Application 220-1 includes one or more software applications that may beprovided to or accessed by user device 105. Application 220-1 mayeliminate a need to install and execute the software applications onuser device 105. For example, application 220-1 may include softwareassociated with vehicle platform 115 and/or any other software capableof being provided via cloud computing environment 210. In someimplementations, one application 220-1 may send/receive informationto/from one or more other applications 220-1, via virtual machine 220-2.

Virtual machine 220-2 includes a software implementation of a machine(e.g., a computer) that executes programs like a physical machine.Virtual machine 220-2 may be either a system virtual machine or aprocess virtual machine, depending upon use and degree of correspondenceto any real machine by virtual machine 220-2. A system virtual machinemay provide a complete system platform that supports execution of acomplete operating system (“OS”). A process virtual machine may executea single program and may support a single process. In someimplementations, virtual machine 220-2 may execute on behalf of a user(e.g., a user of user device 105 or an operator of vehicle platform115), and may manage infrastructure of cloud computing environment 210,such as data management, synchronization, or long-duration datatransfers.

Virtualized storage 220-3 includes one or more storage systems and/orone or more devices that use virtualization techniques within thestorage systems or devices of computing resource 220. In someimplementations, within the context of a storage system, types ofvirtualizations may include block virtualization and filevirtualization. Block virtualization may refer to abstraction (orseparation) of logical storage from physical storage so that the storagesystem may be accessed without regard to physical storage orheterogeneous structure. The separation may permit administrators of thestorage system flexibility in how the administrators manage storage forend users. File virtualization may eliminate dependencies between dataaccessed at a file level and a location where files are physicallystored. This may enable optimization of storage use, serverconsolidation, and/or performance of non-disruptive file migrations.

Hypervisor 220-4 may provide hardware virtualization techniques thatallow multiple operating systems (e.g., “guest operating systems”) toexecute concurrently on a host computer, such as computing resource 220.Hypervisor 220-4 may present a virtual operating platform to the guestoperating systems and may manage the execution of the guest operatingsystems. Multiple instances of a variety of operating systems may sharevirtualized hardware resources.

Network 230 includes one or more wired and/or wireless networks. Forexample, network 230 may include a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a local area network (LAN),a wide area network (WAN), a metropolitan area network (MAN), atelephone network (e.g., the Public Switched Telephone Network (PSTN)),a private network, an ad hoc network, an intranet, the Internet, a fiberoptic-based network, and/or the like, and/or a combination of these orother types of networks.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 2. Furthermore, two or more devices shown in FIG. 2 may beimplemented within a single device, or a single device shown in FIG. 2may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 200 may perform one or more functions described as beingperformed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to user device 105, vehicle platform 115, and/orcomputing resource 220. In some implementations, user device 105,vehicle platform 115, and/or computing resource 220 may include one ormore devices 300 and/or one or more components of device 300. As shownin FIG. 3, device 300 may include a bus 310, a processor 320, a memory330, a storage component 340, an input component 350, an outputcomponent 360, and a communication interface 370.

Bus 310 includes a component that permits communication among thecomponents of device 300. Processor 320 is implemented in hardware,firmware, or a combination of hardware and software. Processor 320 is acentral processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), a microprocessor, a microcontroller,a digital signal processor (DSP), a field-programmable gate array(FPGA), an application-specific integrated circuit (ASIC), or anothertype of processing component. In some implementations, processor 320includes one or more processors capable of being programmed to perform afunction. Memory 330 includes a random-access memory (RAM), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 320.

Storage component 340 stores information and/or software related to theoperation and use of device 300. For example, storage component 340 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, and/or a solid-state disk), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 350 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 350 mayinclude a sensor for sensing information (e.g., a global positioningsystem (GPS) component, an accelerometer, a gyroscope, and/or anactuator). Output component 360 includes a component that providesoutput information from device 300 (e.g., a display, a speaker, and/orone or more light-emitting diodes (LEDs)).

Communication interface 370 includes a transceiver-like component (e.g.,a transceiver and/or a separate receiver and transmitter) that enablesdevice 300 to communicate with other devices, such as via a wiredconnection, a wireless connection, or a combination of wired andwireless connections. Communication interface 370 may permit device 300to receive information from another device and/or provide information toanother device. For example, communication interface 370 may include anEthernet interface, an optical interface, a coaxial interface, aninfrared interface, a radio frequency (RF) interface, a universal serialbus (USB) interface, a Wi-Fi interface, a cellular network interface,and/or the like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes based on processor 320 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 330 and/or storage component 340. Acomputer-readable medium is defined herein as a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 are provided asan example. In practice, device 300 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3. Additionally, or alternatively, aset of components (e.g., one or more components) of device 300 mayperform one or more functions described as being performed by anotherset of components of device 300.

FIG. 4 is a flow chart of an example process 400 for utilizing machinelearning and feature selection to classify driving behavior. In someimplementations, one or more process blocks of FIG. 4 may be performedby a device (e.g., vehicle platform 115). In some implementations, oneor more process blocks of FIG. 4 may be performed by another device or agroup of devices separate from or including the device, such as a userdevice (e.g., user device 105).

As shown in FIG. 4, process 400 may include receiving vehicle operationdata associated with operation of a plurality of vehicles (block 410).For example, the device (e.g., using computing resource 220, processor320, communication interface 370, and/or the like) may receive vehicleoperation data associated with operation of a plurality of vehicles, asdescribed above. The vehicle operation data may include data identifyingaccelerations of the plurality of vehicles, speeds of the plurality ofvehicles, distances of the plurality of vehicles from other vehicles,pedal positions of the plurality of vehicles, steering angles of theplurality of vehicles, engine revolutions-per-minute of the plurality ofvehicles, and/or the like. The vehicle operation data may be receivedfrom user devices associated with the plurality of vehicles, inertialmeasurement units associated with the plurality of vehicles, three-axisaccelerometers associated with the plurality of vehicles, globalpositioning system (GPS) devices associated with the plurality ofvehicles, engine control modules associated with the plurality ofvehicles, video cameras associated with the plurality of vehicles,and/or the like.

As further shown in FIG. 4, process 400 may include processing thevehicle operation data to generate processed vehicle operation data fora particular time period (block 420). For example, the device (e.g.,using computing resource 220, processor 320, memory 330, and/or thelike) may process the vehicle operation data to generate processedvehicle operation data for a particular time period, as described above.In some implementations, processing the vehicle operation data togenerate the processed vehicle operation data may include process 400applying a moving average, based on the particular time period, to thevehicle operation data to generate a low-pass version of the vehicleoperation data; and adding the low-pass version of the vehicle operationdata to the vehicle operation data to generate the processed vehicleoperation data.

As further shown in FIG. 4, process 400 may include extracting aplurality of features from the processed vehicle operation data (block430). For example, the device (e.g., using computing resource 220,processor 320, storage component 340, and/or the like) may extract aplurality of features from the processed vehicle operation data, asdescribed above. In some implementations, extracting the plurality offeatures from the processed vehicle operation data may include process400 computing a plurality of statistics based on the processed vehicleoperation data.

As further shown in FIG. 4, process 400 may include training machinelearning models, with the plurality of features, to generate trainedmachine learning models (block 440). For example, the device (e.g.,using computing resource 220, processor 320, memory 330, and/or thelike) may train machine learning models, with the plurality of features,to generate trained machine learning models, as described above. In someimplementations, training the machine learning models with the pluralityof features may include process 400 utilizing a nested cross-validationto tune the plurality of features for the machine learning models and toevaluate the machine learning models; performing a preliminary analysis,with a first random forest model, to tune the plurality of features; andutilizing a second random forest model to classify the plurality offeatures and to generate the trained machine learning models. Each ofthe machine learning models may include a random forest machine learningmodel.

As further shown in FIG. 4, process 400 may include generating modeloutputs based on training the machine learning models (block 450). Forexample, the device (e.g., using computing resource 220, processor 320,storage component 340, input component 350, output component 360,communication interface 370, and/or the like) may generate model outputsbased on training the machine learning models, as described above.

As further shown in FIG. 4, process 400 may include processing theplurality of features, with a feature selection model and based on themodel outputs, to select sets of features from the plurality of features(block 460). For example, the device (e.g., using computing resource220, processor 320, memory 330, storage component 340, and/or the like)may process the plurality of features, with a feature selection modeland based on the model outputs, to select sets of features from theplurality of features, as described above. In some implementations,processing the plurality of features, with the feature selection modeland based on the model outputs, to select the sets of features, mayinclude process 400 estimating an importance of each of the plurality offeatures based on an average Gini importance associated with each of theplurality of features; and selecting the sets of features from theplurality of features based on the importance estimated for each of theplurality of features.

In some implementations, processing the plurality of features, with thefeature selection model and based on the model outputs, to select thesets of features, may include process 400 ranking each of the pluralityof features based on an average Gini importance associated with each ofthe plurality of features to generate rankings of the plurality offeatures; filtering the rankings of the plurality of features togenerate filtered rankings of the plurality of features; determiningvalidation accuracies associated with plurality of features based on thefiltered rankings of the plurality of features; and selecting the setsof features, from the plurality of features, based on the validationaccuracies associated with plurality of features.

As further shown in FIG. 4, process 400 may include processing the setsof features, with the trained machine learning models, to generateindications of driving behavior and reliabilities of the indications ofdriving behavior (block 470). For example, the device (e.g., usingcomputing resource 220, processor 320, memory 330, and/or the like) mayprocess the sets of features, with the trained machine learning models,to generate indications of driving behavior and reliabilities of theindications of driving behavior, as described above. Each of theindications of driving behavior includes information identifying one ofaggressive driving behavior or normal driving behavior.

As further shown in FIG. 4, process 400 may include selecting a set offeatures, from the sets of features, based on the indications of drivingbehavior and the reliabilities of the indications of driving behavior,wherein a user device associated with a particular vehicle is capable ofcalculating the set of features for the particular time period (block480). For example, the device (e.g., using computing resource 220,processor 320, storage component 340, and/or the like) may select a setof features, from the sets of features, based on the indications ofdriving behavior and the reliabilities of the indications of drivingbehavior, as described above. In some implementations, a user deviceassociated with a particular vehicle may be capable of calculating theset of features for the particular time period.

Process 400 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In some implementations, process 400 may further include providing, tothe user device, a request to calculate the set of features for theparticular time period, where the user device is capable of calculatingthe set of features for the particular time period and for theparticular vehicle based on the request; receiving, from the userdevice, data identifying the set of features for the particular timeperiod; and processing data identifying the set of features, with one ofthe trained machine learning models, to generate an indication ofdriving behavior associated with the particular vehicle.

In some implementations, process 400 may further include performing oneor more actions based on the indication of driving behavior associatedwith the particular vehicle. The one or more actions may includeproviding, to the user device, the indication of driving behaviorassociated with the particular vehicle; providing the indication ofdriving behavior to a particular device associated with an insurer ofthe particular vehicle; providing, to the user device, an instruction toaddress the driving behavior; causing the particular vehicle to bedisabled based on the indication of driving behavior; determining adriver risk score, for a driver of the particular vehicle, based on theindication of driving behavior; retraining at least one of the machinelearning models based on the indication of driving behavior; causing adriver training program, focused the driving behavior, to be scheduledfor the driver of the particular vehicle; causing the particular vehicleto operate in an autonomous mode until the driver of the particularvehicle completes particular training; and/or the like.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4. Additionally, or alternatively, two or more of theblocks of process 400 may be performed in parallel.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations may be made inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term “component” is intended to be broadly construedas hardware, firmware, or a combination of hardware and software.

To the extent the aforementioned implementations collect, store, oremploy personal information of individuals, it should be understood thatsuch information shall be used in accordance with all applicable lawsconcerning protection of personal information. Additionally, thecollection, storage, and use of such information can be subject toconsent of the individual to such activity, for example, through wellknown “opt-in” or “opt-out” processes as can be appropriate for thesituation and type of information. Storage and use of personalinformation can be in an appropriately secure manner reflective of thetype of information, for example, through various encryption andanonymization techniques for particularly sensitive information.

A user interface may include a graphical user interface, a non-graphicaluser interface, a text-based user interface, and/or the like. A userinterface may provide information for display. In some implementations,a user may interact with the information, such as by providing input viaan input component of a device that provides the user interface fordisplay. In some implementations, a user interface may be configurableby a device and/or a user (e.g., a user may change the size of the userinterface, information provided via the user interface, a position ofinformation provided via the user interface, etc.). Additionally, oralternatively, a user interface may be pre-configured to a standardconfiguration, a specific configuration based on a type of device onwhich the user interface is displayed, and/or a set of configurationsbased on capabilities and/or specifications associated with a device onwhich the user interface is displayed.

It will be apparent that systems and/or methods described herein may beimplemented in different forms of hardware, firmware, or a combinationof hardware and software. The actual specialized control hardware orsoftware code used to implement these systems and/or methods is notlimiting of the implementations. Thus, the operation and behavior of thesystems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of various implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of various implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Further, asused herein, the article “the” is intended to include one or more itemsreferenced in connection with the article “the” and may be usedinterchangeably with “the one or more.” Furthermore, as used herein, theterm “set” is intended to include one or more items (e.g., relateditems, unrelated items, a combination of related and unrelated items,etc.), and may be used interchangeably with “one or more.” Where onlyone item is intended, the phrase “only one” or similar language is used.Also, as used herein, the terms “has,” “have,” “having,” or the like areintended to be open-ended terms. Further, the phrase “based on” isintended to mean “based, at least in part, on” unless explicitly statedotherwise. Also, as used herein, the term “or” is intended to beinclusive when used in a series and may be used interchangeably with“and/or,” unless explicitly stated otherwise (e.g., if used incombination with “either” or “only one of”).

1. A method, comprising: receiving, by a device, vehicle operation dataassociated with operation of a plurality of vehicles; processing, by thedevice, the vehicle operation data to generate processed vehicleoperation data for a first time period; extracting, by the device, aplurality of features from the processed vehicle operation data;training, by the device, machine learning models, with the plurality offeatures, to generate trained machine learning models; generating, bythe device, model outputs based on training the machine learning models;processing, by the device, the plurality of features, with a featureselection model and based on the model outputs, to select sets offeatures from the plurality of features; processing, by the device, thesets of features, with the trained machine learning models, to generatefirst indications of driving behavior and reliabilities of the firstindications of driving behavior; selecting, by the device, a set offeatures, from the sets of features, based on the first indications ofdriving behavior and the reliabilities of the first indications ofdriving behavior; providing, by the device and to a user deviceassociated with a particular vehicle, a request to calculate theselected set of features for a second time period, wherein the vehicleoperation data is received, by the user device, from the particularvehicle based on the request, for the second time period; receiving, bythe device and from the user device, data identifying the selected setof features for the second time period; and processing, by the device,the data identifying the selected set of features to generate a secondindication of driving behavior associated with the particular vehicle.2. (canceled)
 3. The method of claim 1, further comprising: performingone or more actions based on the second indication of driving behaviorassociated with the particular vehicle.
 4. The method of claim 1,further comprising one or more of: providing, to the user device, thesecond indication of driving behavior associated with the particularvehicle; providing the second indication of driving behavior associatedwith the particular vehicle to a particular device associated with aninsurer of the particular vehicle; providing, to the user device, aninstruction to address driving behavior based on the second indicationof driving behavior associated with the particular vehicle; causing theparticular vehicle to be disabled based on the second indication ofdriving behavior associated with the particular vehicle; determining adriver risk score, for a driver of the particular vehicle, based on thesecond indication of driving behavior associated with the particularvehicle; or retraining at least one of the machine learning models basedon the second indication of driving behavior associated with theparticular vehicle.
 5. The method of claim 1, wherein each of the firstindications of driving behavior includes information identifying one of:aggressive driving behavior, or normal driving behavior.
 6. The methodof claim 1, wherein processing the vehicle operation data to generatethe processed vehicle operation data comprises: applying a movingaverage, based on the particular first time period, to the vehicleoperation data to generate a low-pass version of the vehicle operationdata; and adding the low-pass version of the vehicle operation data tothe vehicle operation data to generate the processed vehicle operationdata.
 7. The method of claim 1, wherein extracting the plurality offeatures from the processed vehicle operation data comprises: computinga plurality of statistics based on the processed vehicle operation data,wherein the plurality of statistics correspond to the plurality offeatures.
 8. A device, comprising: one or more memories; and one or moreprocessors, communicatively coupled to the one or more memories,configured to: receive vehicle operation data associated with operationof a plurality of vehicles; process the vehicle operation data togenerate processed vehicle operation data for a first time period;extract a plurality of features from the processed vehicle operationdata; train machine learning models, with the plurality of features, togenerate trained machine learning models, wherein training the machinelearning models generates model outputs; process the plurality offeatures, with a feature selection model and based on the model outputs,to select sets of features from the plurality of features; process thesets of features, with the trained machine learning models, to generatefirst indications of driving behavior and reliabilities of the firstindications of driving behavior; select a set of features, from the setsof features, based on the first indications of driving behavior and thereliabilities of the first indications of driving behavior; provide, toa user device associated with a particular vehicle, a request tocalculate the selected set of features for a second time period, whereinvehicle operation data of the particular vehicle is received, by theuser device, from the particular vehicle based on the request, for thesecond time period. receive, from the user device, data identifying theselected set of features for the second time period; and determine asecond indication of driving behavior associated with the particularvehicle based on the data identifying the selected set of features. 9.The device of claim 8, wherein the one or more processors, when trainingthe machine learning models with the plurality of features, areconfigured to: utilize a nested cross-validation to tune the pluralityof features for the machine learning models and to evaluate the machinelearning models; perform a preliminary analysis, with a first randomforest model, to tune the plurality of features; and utilize a secondrandom forest model to classify the plurality of features and togenerate the trained machine learning models.
 10. The device of claim 8,wherein the one or more processors, when processing the plurality offeatures, with the feature selection model and based on the modeloutputs, to select the sets of features, are configured to: estimate animportance of each of the plurality of features based on an average Giniimportance associated with each of the plurality of features; and selectthe sets of features from the plurality of features based on theimportance estimated for each of the plurality of features.
 11. Thedevice of claim 8, wherein the one or more processors, when processingthe plurality of features, with the feature selection model and based onthe model outputs, to select the sets of features, are configured to:rank each of the plurality of features based on an average Giniimportance associated with each of the plurality of features to generaterankings of the plurality of features; filter the rankings of theplurality of features to generate filtered rankings of the plurality offeatures; determine validation accuracies associated with the pluralityof features based on the filtered rankings of the plurality of features;and select the sets of features, from the plurality of features, basedon the validation accuracies associated with plurality of features. 12.The device of claim 8, wherein each of the machine learning modelsincludes a random forest machine learning model.
 13. The device of claim8, wherein the vehicle operation data associated with the plurality ofvehicles includes data identifying one or more of: accelerations of theplurality of vehicles, speeds of the plurality of vehicles, distances ofthe plurality of vehicles from other vehicles, pedal positions of theplurality of vehicles, steering angles of the plurality of vehicles, orengine revolutions-per-minute of the plurality of vehicles.
 14. Thedevice of claim 8, wherein the vehicle operation data associated withthe plurality of vehicles is received from one or more of: inertialmeasurement units associated with the plurality of vehicles, three-axisaccelerometers associated with the plurality of vehicles, globalpositioning system (GPS) devices associated with the plurality ofvehicles, engine control modules associated with the plurality ofvehicles, or video cameras associated with the plurality of vehicles.15. A non-transitory computer-readable medium storing instructions, theinstructions comprising: one or more instructions that, when executed byone or more processors, cause the one or more processors to: receivevehicle operation data associated with operation of a plurality ofvehicles; process the vehicle operation data to generate processedvehicle operation data for a first time period; extract a plurality offeatures from the processed vehicle operation data; train machinelearning models, with the plurality of features, to generate trainedmachine learning models and model outputs; process the plurality offeatures, with a feature selection model and based on the model outputs,to select sets of features from the plurality of features; process thesets of features, with the trained machine learning models, to generatefirst indications of driving behavior and reliabilities of the firstindications of driving behavior; select a set of features, from the setsof features, based on the first indications of driving behavior and thereliabilities of the first indications of driving behavior; provide, toa user device associated with a particular vehicle, a request tocalculate the selected set of features for a second time period, whereinthe vehicle operation data of the particular vehicle is received, by theuser device, from the particular vehicle based on the request, for thesecond time period; receiving, from the user device, data identifyingthe selected set of features for the second time period; determine,based on the data identifying the selected set of features, a secondindication of driving behavior associated with a particular vehicle; andperform one or more actions based on the second indication of drivingbehavior associated with the particular vehicle.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the one or moreinstructions, that cause the one or more processors to perform the oneor more actions, cause the one or more processors to one or more of:provide, to the user device, the second indication of driving behaviorassociated with the particular vehicle; provide the second indication ofdriving behavior associated with the particular vehicle to a particulardevice associated with an insurer of the particular vehicle; provide, tothe user device, an instruction to address driving behavior based on thesecond indication of driving behavior associated with the particularvehicle; cause the particular vehicle to be disabled based on the secondindication of driving behavior associated with the particular vehicle;determine a driver risk score, for a driver of the particular vehicle,based on the second indication of driving behavior associated with theparticular vehicle; retrain at least one of the machine learning modelsbased on the second indication of driving behavior associated with theparticular vehicle; cause a driver training program, focused on drivingbehavior based on the second indication of driving behavior associatedwith the particular vehicle, to be scheduled for the driver of theparticular vehicle; or cause the particular vehicle to operate in anautonomous mode until the driver of the particular vehicle completesparticular training.
 17. The non-transitory computer-readable medium ofclaim 15, wherein the second indication of driving behavior associatedwith the particular vehicle includes information identifying one of:aggressive driving behavior, or normal driving behavior.
 18. Thenon-transitory computer-readable medium of claim 15, wherein the one ormore instructions, that cause the one or more processors to extract theplurality of features from the processed vehicle operation data, causethe one or more processors to: compute a plurality of statistics basedon the processed vehicle operation data, wherein the plurality ofstatistics correspond to the plurality of features.
 19. Thenon-transitory computer-readable medium of claim 15, wherein the one ormore instructions, that cause the one or more processors to train themachine learning models with the plurality of features, cause the one ormore processors to: utilize a nested cross-validation to tune theplurality of features for the machine learning models and to evaluatethe machine learning models; perform a preliminary analysis, with afirst random forest model, to tune the plurality of features; andutilize a second random forest model to classify the plurality offeatures and to generate the trained machine learning models.
 20. Thenon-transitory computer-readable medium of claim 15, wherein the one ormore instructions, that cause the one or more processors to process theplurality of features, with the feature selection model and based on themodel outputs, to select the sets of features, cause the one or moreprocessors to: estimate an importance of each of the plurality offeatures based on an average Gini importance associated with each of theplurality of features; and select the sets of features from theplurality of features based on the importance estimated for each of theplurality of features.
 21. The method of claim 1, wherein processing theplurality of features, with the feature selection model and based on themodel outputs, to select the sets of features from the plurality offeatures, comprises: ranking each of the plurality of features based onan importance associated with each of the plurality of features togenerate rankings of the plurality of features; filtering the rankingsof the plurality of features to generate filtered rankings of theplurality of features; determining validation accuracies associated withthe plurality of features based on the filtered rankings of theplurality of features; and selecting the sets of features, from theplurality of features, based on the validation accuracies associatedwith plurality of features.